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Online Prediction of Electric Vehicle Battery Failure Using LSTM Network

Author

Listed:
  • Xuemei Li

    (School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Hao Chang

    (School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China
    School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Ruichao Wei

    (School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China)

  • Shenshi Huang

    (School of Architectural Engineering, Shenzhen Polytechnic, Shenzhen 518055, China)

  • Shaozhang Chen

    (School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Zhiwei He

    (School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China
    School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China)

  • Dongxu Ouyang

    (College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China)

Abstract

The electric vehicle industry is developing rapidly as part of the global energy structure transformation, which has increased the importance of overcoming power battery safety issues. In this paper, first, we study the relationship between different types of vehicle faults and battery data based on the actual vehicle operation data in the big data supervisory platform of new energy vehicles. Second, we propose a method to realize the online prediction of electric vehicle battery faults, based on a Long Short-Term Memory (LSTM). Third, we carry out prediction research for two kinds of faults: low State of Charge (SOC) alarm and insulation alarm. Last, we show via experimental results that the model based on the LSTM network can effectively predict battery faults with an accuracy of more than 85%. Through this research, it is possible to complete online pre-processing of vehicle operation data and fault prediction of power batteries, improve vehicle monitoring capabilities and ensure the safety of electric vehicle use.

Suggested Citation

  • Xuemei Li & Hao Chang & Ruichao Wei & Shenshi Huang & Shaozhang Chen & Zhiwei He & Dongxu Ouyang, 2023. "Online Prediction of Electric Vehicle Battery Failure Using LSTM Network," Energies, MDPI, vol. 16(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4733-:d:1172019
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    References listed on IDEAS

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    1. Huiping Jiang & Rui Jiao & Zequn Wang & Ting Zhang & Licheng Wu & Ning Cai, 2021. "Construction and Analysis of Emotion Computing Model Based on LSTM," Complexity, Hindawi, vol. 2021, pages 1-12, February.
    2. Hong, Jichao & Wang, Zhenpo & Yao, Yongtao, 2019. "Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Hyunsoo Kim & Youngwoo Kwon & Yeol Choi, 2020. "Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
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    Cited by:

    1. Tao Yan & Jizhong Chen & Dong Hui & Xiangjun Li & Delong Zhang, 2024. "The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model," Sustainability, MDPI, vol. 16(5), pages 1-14, February.

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